Nonparametric blind SAR image super resolution based on combination of the compressive sensing and sparse priors.

Highlights • A novel approach is proposed for the nonparametric blind single image super resolution of SAR. • Combination of an adaptive compressive sensing and sparse priors is the fundamental idea. • A new approach based on the conjugate gradient least squares method is proposed. • Proposed method achieves the state-of-the-art performance in the PSF kernel estimation accuracy. This paper by proposing a novel approach, is one the first works that addresses the highly ill-posed problem of nonparametric blind single image super resolution (SISR) of the synthetic aperture radar (SAR) images. Combination of an adaptive compressive sensing (CS) technique and some effective sparse priors, as a powerful regularizer in the both high resolution (HR) image reconstruction and the point spread function (PSF) estimation domains is the fundamental idea of the proposed method. This task is formulated as a new cost function to be minimized with respect to an intermediate reconstructed HR image patch and a nonparametric PSF kernel, according to the alternative minimization (AM) algorithm. To solve the optimization of cost function, a numerical scheme based on the conjugate gradient least squares (CGLS) method is proposed. Experimental results for the both synthetic and realistic low resolution (LR) SAR images demonstrate that the proposed method achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]